Developing teamwork skills and creativity is becoming increasingly important in higher education. Brainstorming activities can support the acquisition of such transversal skills by encouraging open dialogue in collaborative design processes. In a successful brainstorming, participants propose diverse ideas, combine them, and elaborate new propositions by taking inspiration in the propositions of their teammates. However, the cognitive effect of idea fixation may hinder this process. Idea fixations happens when participants focus on a limited set of ideas and are unable to think about unrelated alternatives. This effect lowers the creativity of the group and prevents the participants to consider diverse aspects of the problem at hand, which is an important mechanism in design-based learning. Leveraging the recent development in Large Language Models (LLM), we propose to integrate a Generative-AI agent to introduce new ideas into a brainstorming process supported by a digital application. Such agent can be designed to bring new topics to be considered that the students may not have brought up themselves. Our study aims to examine how such AI-generated ideas may influence the ideas proposed anonymously by the participants, and ultimately, the outcome of the activity. We use a quasi-experimental design with two conditions: one in which only the students generate ideas (control), another where they receive AI-generated ideas along with their own. We conduct this experiment as a part of a design thinking project carried out by computer science students. Using a qualitative coding combined with Epistemic Network Analysis, we investigate the dissemination of the topics brought by the Generative-AI agent within the ideas of the participants. We also present and discuss practical aspects of the intervention and its implementation by educators in their teaching. Our findings are contributing to the understanding of humanAI interactions in the context of collaborative ideation in educational settings. They can inform the design of new interaction mechanisms with AI agents to support creativity. Furthermore, by introducing previously unconsidered topics to these activities, these AI-based agents can support groups of students in considering a wider range of aspects of the matter at hand, supporting the development of critical thinking. These mechanisms may be useful to educators and facilitators looking for solutions to overcome idea fixation and increase creativity in brainstorming.

In the current digital era, smartphones have evolved beyond simple communication tools into essential multifunctional devices that particularly pervade the daily lives of young adults. This article systematically reviews the impact of smartphone usage on the digital wellbeing of university students. Through a structured review of empirical studies, we examine the relationship between smartphone use and various aspects of digital wellbeing, such as sleep deprivation, attention deficits, and social challenges. We discuss the methodologies employed in these studies, analyze the data collected, and summarize and categorize the findings within the current research. This comprehensive synthesis aims to inform scholars and policymakers of the impact of smartphone usage on digital wellbieng and suggests potential directions for future research into digital wellbeing.

Critical thinking is vital in every education system. Digital tools like Large Language Models (LLMs), exemplified by ChatGPT, are part of a new scheme of education; studying its influence on critical thinking (CT) is a rising research field with a need for literature exploring children and diverse populations. This study examines (1) the influence of ChatGPT usage on text-based activities in the CT dimensions of analysis, inference, & explanation in 10–12 years old children and (2) the role of urban-rural educational disparities in its utilization. Employing a two-by-two quasi-experimental design, students from both an urban and a rural school in Mexico participated in sustainable energy literacy activities implemented in the educational social media platform “Graasp.” The three session study juxtaposed the open answers using traditional definitions or a tailored chatbot powered by ChatGPT. Pretest-posttest CT and energy literacy were applied. A repeated measured ANOVA uni-dimensional analysis showed significance within subjects of the analysis dimension (p=0.003), indicating a possible influence of ChatGPT throughout the intervention; no significance was found within subjects in the CT integral test. Between subjects, significant results in CT were obtained, showing differences in both educational settings. Both groups demonstrated gains in sustainable energy literacy, as expected; Depth of answers was always favored when urban students used ChatGPT. Rural students had higher depth while using definitions overall, but the depth of the ChatGPT intervention answers increased after the first session, showing interesting results regarding technological adaptation. A strong preference for the chatbot interface was developed, with 95% of students favoring the chatbot by the end of the intervention. Further research is recommended to explore the role of the usage of ChatGPT in critical thinking dimensions.

Social media have become an indispensable part of daily life, particularly among university students, who regularly browse social news feeds in their spare time. Due to their pervasiveness, social media platforms provide an opportunity for influencing user behavior and encouraging informal learning. In this paper, we present an experiment using an online video recommendation application designed to blend micro-informative content with general content according to user preferences and activity history. Based on a one-week study, we conclude that injecting micro-informative content into video streaming platforms has the potential to improve the perceived satisfaction of users and can act as a potential catalyst to motivate users to consume more informative content online.

Over the past decade, the use of chatbots for educational purposes has gained considerable traction. A similar trend has been observed in social coding platforms, where automated agents support software developers with tasks such as performing code reviews. While incorporating code reviews and social coding platforms into software engineering education has been found to be beneficial, challenges such as steep learning curves and privacy considerations are barriers to their adoption. Furthermore, no study has addressed the role chatbots play in supporting code reviews as a pedagogical tool. To help address this gap, we developed an online learning application that simulates the code review features available on social coding platforms and allows instructors to interact with students using chatbot identities. We then embedded this application within a lesson on software engineering best practices and conducted a controlled in-class experiment. This experiment examined the effect that explaining content via chatbot identities had on three aspects: (i) students’ perceived usability of the lesson, (ii) their engagement with the code review process, and (iii) their learning gains. While our findings show that it is feasible to simulate the code review process within an online learning platform and achieve good usability, our quantitative analysis did not yield significant differences across treatment conditions for any of the aspects considered. Nevertheless, our qualitative results suggest that students expect explicit feedback when performing this type of exercise and could thus benefit from automated replies provided by an interactive chatbot. We propose to build on our current findings to further explore this line of research in future work.

The trend towards generalized blended and active learning approaches in engineering education is redefining the requirements and the deployment schemes for digital education platforms. This paper presents a quadriptych techno-pedagogical model highlighting the features and the services an effective digital education platform should provide to accompany educators in their journey towards the creation of rich—and possibly open—educational resources, as well as their integration into blended and active learning scenarios. An implementation of this model for a novel digital education platform called Graasp is discussed and three use cases are detailed. These use cases show that a large variety of active pedagogical scenarios can be implemented, if easily accessible and comprehensive digital education platforms are available.

Peer code review has proven to be a valuable tool in software engineering. However, integrating code reviews into educational contexts is particularly challenging due to the complexity of both the process and popular code review tools. We propose to address this challenge by designing a code review application (CRA) aimed at teaching the code review process directly within existing online learning platforms. Using the CRA, instructors can scaffold online lessons that introduce the code review process to students through code snippets, following a format resembling computational notebooks. We refer to this online lesson format as the code review notebook format. Through a case study comprising an online lesson on code quality standards completed by 23 university students, we evaluated the usability of the CRA and the code review notebook format, obtaining positive results for both. These results are a first step toward integrating code review notebooks into software engineering education.

Astrobots are robotic artifacts whose swarms are used in astrophysical studies to generate the map of the observable universe. These swarms have to be coordinated with respect to various desired observations. Such coordination is so complicated that distributed swarm controllers cannot always coordinate enough astrobots to fulfill the minimum data desired to be obtained in the course of observations. Thus, a convergence verification is necessary to check the suitability of coordination before its execution. However, a formal verification method does not exist for this purpose. In this article, we instead use machine learning to predict the convergence of astrobots swarm. As the first solution to this problem, we propose a weighted$k$-NN-based algorithm that requires the initial status of a swarm and its observational targets to predict its convergence. Our algorithm learns to predict based on the coordination data obtained from previous coordination of the desired swarm. This method first generates a convergence probability for each astrobot based on a distance metric. Then, these probabilities are transformed to either a complete or an incomplete categorical result. The method is applied to two typical swarms, including 116 and 487 astrobots. It turns out that the correct prediction of successful coordination may be up to 80% of overall predictions. Thus, these results witness the efficient accuracy of our predictive convergence analysis strategy.Note to Practitioners—Observatories involved in the generation of spectroscopic surveys always encounter limited resources to check the throughputs of their planned observations before their executions. The information yielded by an observation directly depends on the convergence rate of the observatory’s astrobots in that particular observation. Namely, if the astrobots’ convergence rate is below a minimum, then the observation has to be revoked and replanned. Thus, one may define another observation that fulfills the minimum-information requirement. There has been yet no analytical tool developed to verify the convergence rate of the coordination computed by the state-of-the-art trajectory planners of astrobots swarms. Thus, we propose to use a machine learning scheme to predict the desired convergence rate instead of involving in the infeasible process of finding its exact value. This method is a supervised method that requires the target-to-astrobot assignments table of an observation. The algorithm also needs a data set including previous coordination results of various observations of a particular swarm. The simulated scenarios manifest magnificent accuracies in the convergence predictions of some astrobots swarms corresponding to modern spectroscopic surveys, such as SDSS-V (including ~500 astrobots). Our strategy is based on the smallest subset of the astrobots’ features that have a pivotal role in convergence rates, say, the projected positions of targets on a hosting telescope’s focal plane. We argue that more explorations have to be considered to find other important features, such as the motion direction of each astrobot, which may even further improve the obtained prediction accuracies.

Chatbots have long been advocated for computer-assisted language learning systems to support learners with conversational practice. A particular challenge in such systems is explaining mistakes stemming from ambiguous grammatical constructs. Misplaced modifiers, for instance, do not make sentences ungrammatical, but introduce ambiguity through the misplacement of an adverb or prepositional phrase. In certain cases, the ambiguity gives rise to humor, which can serve to illustrate the mistake itself. We conducted an online experiment with 400 native English speakers to explore the use of a chatbot to harness such humor. In an interaction resembling an advanced grammar exercise, the chatbot presented participants with a phrase containing a misplaced modifier, explained the ambiguity in the phrase, acknowledged (or ignored) the humor that the ambiguity gave rise to, and suggested a correction. Participants then completed a questionnaire, rating the chatbot with respect to ten traits. A quantitative analysis showed a significant increase in how participants rated the chatbot’s personality, humor, and friendliness when it acknowledged the humor arising from the misplaced modifier. This effect was observed whether the acknowledgment was conveyed using verbal, nonverbal (emoji), or mixed cues.

Spending an uncontrolled quantity and quality of time on digital news and social media platforms can negatively influence mental health and decrease cognitive abilities. In this paper, we propose a sequential news recommendation system employing deep reinforcement learning to capture the user’s short and long-term interests while blending social news with micro-learning informative news items that can help users derive useful outcomes out of their online presence. In the absence of a publicly available dataset, we developed a simulation model to synthesize data and evaluate the proposed news recommendation system. We train and evaluate our model on synthesized data and show an improvement in user satisfaction.

Humanitarian organisations provide invaluable work to improve the lives of individuals impacted by natural and anthropogenic hazards. While humanitarian organisations are highly knowledge intensive, they often fail to manage knowledge effectively. Providing adequate incentives to foster knowledge sharing on a knowledge management system is a challenge for many organisations. It is especially important in the humanitarian context where organisational efficiency leads to saving more lives. We argue that gamification (i.e., the integration of game-like features, such as points or badges in non-game systems) is a viable solution to address the lack of knowledge sharing often encountered. To do so, we embark on a design science journey with Doctors Without Borders to investigate how gamification within (e.g., using a personal profile) and outside (e.g., using a connected ambient object) a knowledge management system can improve knowledge sharing. Our findings demonstrate that well-designed gamification can increase engagement and knowledge sharing, in particular for altruistic individuals.

This paper discusses an ongoing initiative aimed at promoting and implementing digital STEM education at secondary schools in Africa. This initiative, coined Go-Lab Goes Africa (GO-GA), is an innovation action supported by the European Commission through its H2020 Framework Programme for Research and Technological Development in Information and Communication Technologies (ICT). The general vision and the implementation strategy are outlined in detail, as well as the challenges faced and results achieved during its first year.

Despite the traction gained by the open data movement and the rise of big data and learning analytics in education, there is limited support for researchers in education to generate, access, and share experimental data using openly-available digital education platforms. To explore how this gap could be addressed and elicit requirements, we conducted a survey with 40 researchers in the field of technology-enhanced learning, examining their experience and needs handling research data. Drawing on the results of our survey, we devised a set of features that educational platforms should provide to address the identified requirements, enabling researchers in education to run studies within typical learning environments, adhere to legal and ethical frameworks concerning privacy, and share their data confidently with a wider audience. We then categorized these features into five stages that represent the user flow, namely (1) Bootstrapping Research Studies, (2) Ensuring Consent, (3) Gathering Data, (4) Managing Data Sets, and (5) Supporting Open Research and Collaboration. Our aim is to guide forthcoming research and developments to relieve researchers of the burdens of conducting data-sensitive experiments, support the adoption of best practices, and pave the way for open data policies in digital education.

Digital capacity building is a key policy for states to meet the Sustainable Development Goals. This paper1 presents one of the actions carried out by the United Nations Conference on Trade and Development (UNCTAD) in collaboration with the Swiss Federal Institute of Technology in Lausanne (EPFL) to tackle the multifaceted digitization challenges in developing and transition economies by introducing blended learning in one of its flagship executive courses. The main outcome is the obvious but generally neglected importance of focusing on the added values, the competence development, and confidence building for all stakeholders to trigger adoption. These objectives are partially tackled by blending digital and physical communication and awareness channels in face-to-face executive training sessions.

The need to ensure privacy and data protection in educational contexts is driving a shift towards new ways of securing and managing learning records. Although there are platforms available to store educational activity traces outside of a central repository, no solution currently guarantees that these traces are authentic when they are retrieved for review. This paper presents a blueprint for an architecture that employs blockchain technology to sign and validate learning traces, allowing them to be stored in a distributed network of repositories without diminishing their authenticity. Our proposal puts participants in online learning activities at the center of the design process, granting them the option to store learning traces in a location of their choice. Using smart contracts, stakeholders can retrieve the data, securely share it with third parties and ensure it has not been tampered with, providing a more transparent and reliable source for learning analytics. Nonetheless, a preliminary evaluation found that only 56% of teachers surveyed considered tamper-evident storage a useful feature of a learning trace repository. These results motivate further examination with other end users, such as learning analytics researchers, who may have stricter expectations of authenticity for data used in their practice.

Collision avoidance, in particular between robots, is an important component for autonomous robots. It is a necessary component in numerous applications such as humanrobot interaction, automotive or unmanned aerial vehicles. While many collision avoidance algorithms take into account actuation constraints, only a few consider sensing limitations. In this paper, we present a reciprocal collision avoidance algorithm based on the velocity obstacle approach that guarantees collision-free maneuvers even when the robots are only capable to sense their environment within a limited Field Of View (FOV). We also present the challenges associated to sensors with limited FOV, show the conditions under which maneuvering can be safely done, and the modifications that a velocity obstacle approach requires to satisfy such conditions. We provide simulations and real robot experiments to validate our approach.

The humanitarian sector is entering in the network age. The explosion in access to new information and communication technology (ICT) like mobile phones, the internet, and social media, is changing the way people communicate in emergency situations. However, the use of new ICT has not yet been adequately accompanied by an analysis of the challenges it represents for the application of the four humanitarian principles, namely humanity, neutrality, impartiality and independence. These principles provide the foundations for humanitarian action and are central to establishing and maintaining access to affected people. This article addresses that issue and provides a design framework to guide humanitarian agencies towards a principled approach to ICT.

Interacting with knowledge in a timely fashion is critical for the success of humanitarian missions. At the same time, the places where humanitarian action is needed are often those where Internet connection is poor or not available at all, making digital knowledge access difficult. In this paper, we propose a novel knowledge delivery model that relies on a peer-to-peer middleware and uses low-cost computers for local knowledge replication. We have developed a system implementing the model and evaluated it during eight deployments in Médecins Sans Frontières missions. The evaluation demonstrated knowledge delivery abilities of the system and its usefulness for the field staff.

While co-located social media previously has been employed to enhance interaction in community building activities in previous work, its range of effects have not been quantitatively described. In this study we introduce a co-located social media app called SpeakUp to a community building project in the Za'atari Syrian refugee camp in Jordan. To overcome issues such as the lack of Internet access, we purposefully adapted the design of the application and ported its server to a low cost single board computer on a Raspberry Pi. We explore the effects of SpeakUp through field experiments with one control and two treatment groups, as well as with interviews and observations. Our results show that SpeakUp significantly increases refugees' level of participation and sense of community. Importantly, with the use of the application, female and male participants demonstrate no significant differences in participation, showing that the use of such applications can be equalizing. We also found that co-located social media supports asynchronous interaction when outside-the-classroom activities are involved.

Few MOOCs offer laboratory work as part of their educational material, yet it is known that hands-on sessions are important components of science and engineering education. Equally important is understanding how students are using labs as part of their learning activity outside the constraints of space and time. In this work we present the initial results of the usage of a remote lab provided as part of a Control Systems MOOC.

Non-Governmental Organizations in the humanitarian field are particularly knowledge intensive structures. However, they often fail to manage this knowledge efficiently and thus waste resources repeating avoidable mistakes. Providing adequate incentives for knowledge sharing is a central issue in any knowledge management system and is still largely unresolved. In this position paper, we advocate for advances on the topic and lay out a research agenda to address the issue.

We propose an approach for on-line detection of small Unmanned Aerial Vehicles (UAVs) and estimation of their relative positions and velocities in the 3D environment from a single moving camera in the context of sense and avoid systems. This problem is challenging both from a detection point of view, as there are no markers on the targets available, and from a tracking perspective, due to misdetection and false positives. Furthermore, the methods need to be computationally light, despite the complexity of computer vision algorithms, to be used on UAVs with limited payload. To address these issues we propose a multi-staged framework that incorporates fast object detection using an AdaBoost-based approach, coupled with an on-line visual-based tracking algorithm and a recent sensor fusion and state estimation method. Our framework allows for achieving real-time performance with accurate object detection and tracking without any need of markers and customized, high-performing hardware resources.

Research on learning dashboards aims to identify what data is meaningful to different stakeholders in education, and how data can be presented to support sense-making processes. This paper summarizes the main outcomes of a systematic literature review on learning dashboards, in the fields of Learning Analytics and Educational Data Mining. The query was run in five main academic databases and enriched with papers coming from GScholar, resulting in 346 papers out of which 55 were included in the final analysis. Our review distinguishes different kinds of research studies as well as different aspects of learning dashboards and their maturity in terms of evaluation. As the research field is still relatively young, many of the studies are exploratory and proof-of-concept. Among the main open issues and future lines of work in the area of learning dashboards, we identify the need for longitudinal research in authentic settings, as well as studies that systematically compare different dashboard design options.

Developing educational apps that cover a wide range of learning contexts and languages is a challenging task. In this paper, we introduce the AppComposer Web app to address this issue. The AppComposer aims at empowering teachers to easily translate and adapt existing apps that fit their educational contexts. Developers do not need to provide extensive translations and configurations of their apps and can simply follow certain guidelines to make their apps translatable and adaptable by the AppComposer. Since the AppComposer makes use of the standard internationalization specification used by OpenSocial, even external apps can be translated without contacting the original developer.

Timely access to critical information is crucial for any organization operating in situations of emergency. Deploying an adequate information system tailored to specific organizational needs and matched to the organizational structure is essential. To understand the knowledge management needs of Médecins Sans Frontières, one of the leading NGOs in the humanitarian and medical fields, we conducted in total 145 hours of in-depth interviews. This paper presents three identified key requirements for an effective knowledge management system specifically designed for large distributed NGOs, like MSF. Additionally, we introduce a novel social media called Graspeo designed and built from ground up to fulfill these requirements.

In this paper we present a collision avoidance system based on visual detection. Our hardware consists of a Hummingbird quadrotor equipped with a large red marker with two built-in fish-eye cameras. Fusion of the measurements from the two cameras is done using a Gaussian-mixture probability hypothesis density filter, which allows for tracking several aircrafts at the same time. Our collision avoidance algorithm is based on navigation functions designed to cope with cameras characterized by limited field of view. Its mathematical correctness has been proven in a former paper [1]. The collision avoidance maneuver is performed without the vehicles explicitly exchanging information via communication but instead relying solely on on-board sensors. Our system has been validated in an indoor space with four different collision scenarios. Trajectory data was recorded with an external motion capture system and demonstrate good robustness against sensing noise.

In this paper, we briefly describe the goal and activities of the LAK15 workshop on Visual Aspects of Learning analytics.

Technology surrounds us wherever we look, nonetheless it is necessary to facilitate its use in places where a greater public can enjoy, know and learn with it. Techno Museum and Go Lab projects are focused on this goal. As a first prototype to bring technology closer to young students and schools, UNED has developed a Arduino based smart device that facilitates the integration of remote laboratories in learning scenarios. Initially, the Arduino smart device was designed for robotics laboratories, but finally it was decided to expand it to other laboratories. The current prototype is called RGB led: “The color of the light” laboratory. This paper presents its use by teachers and their potential applications as a tool to bring technology to schools and young people who tomorrow will become engineers, scientists and curious citizens.

The expansion of the application of remote laboratories, along with the shift from eLearning 2.0 towards eLearning 3.0, have raised many implementation issues and demanded several considerations in their architectural design, which has long been a missing topic in the literature. In response to these needs, we realize a literature review on the common encountered hardware and software implementation issues nowadays, in order to provide a generic model for developing and implementing modern remote laboratories suited for a wide spectrum of application categories, as well as, for next generation educational systems.

Nowadays, a large number of online laboratories are available and deployed throughout the world. Most of them are sponsored by universities and often only used by their home institution, because they tend to be dedicated to the needs of their creators. However, in many cases these labs provide successful experiences and other teachers would wish to incorporate them into their classroom activities and to embrace Learning-by-Experience methodologies. Currently, they cannot do that effectively without first tailoring the lab experience to their own teaching style and the educational background of their students. This can be a complicated affair, due the closed nature of many online labs and the steep learning curve of the few tools that allow customization. This paper describes the work on an application builder, named App Composer, which aims to make it easy for school teachers to create their own customized applications from existing ones. Different customization levels are provided, each one powered by a different type of composer. For instance, teachers will be able to easily translate an application into a new language, or to start with a template and create a customized version of an application. The App Composer makes use of Graasp and Open Social - a widely-used, interoperable and open technology.

Peer assessment is seen as a powerful supporting tool to achieve scalability in the evaluation of complex assignments in large courses, possibly virtual ones, as in the context of massive open online courses (MOOCs). However, the adoption of peer assessment is slow due in part to the lack of ready-to-use systems. Furthermore, the validity of peer assessment is still under discussion. In this paper, in order to tackle some of these issues, we present as a proof-of-concept of a novel extension of Graasp, a social media platform, to setup a peer assessment activity. We then report a case study of peer assessment using Graasp in a Social Media course with 60 master's level university students and analyze the level of agreement between students and instructors in the evaluation of short individual reports. Finally, to see if both instructor and student evaluations were based on appearance of project reports rather than on content, we conducted a study with 40 kids who rated reports solely on their look. Our results convey the fact that unlike the kid evaluation, which shows a low level of agreement with instructors, student assessment is reliable since the level of agreement between instructors and students was high.

The emergence of social media has created new ways to publish scientific work, foster collaboration, and build professional connections in the research community. The rich data collected in social media platforms has provided new opportunities for assessing scholars' impact other than the traditional citation-based approach. In this paper, we investigate the measures of scholars' influence in academic social media platforms, taking both academic and social impact into account. A real-life dataset collected from Mendeley is used to apply different influence metrics. We first assess the academic influence of scholars based on the scientific impact of their publications using three different measures. Then we investigate their social influence using network centrality metrics. The experiments show that top influencers with high academic impact tend to be senior scholars with many coauthors. Furthermore, academic influence and social influence measures do not strongly correlate with each other, and thus scholars with high academic impact are not necessarily influential from a social point of view. Adding the social dimension could enhance the traditional impact metrics that only take academic influence into account.

A vast number of learning content and tools can be found over Internet. Currently, most of them are ad-hoc solutions which are developed for a particular learning platform or environment. New concepts, such as Widgets, Smart devices, Internet of Thing and learning Clouds, are ideas whose goals is the creation of shareable online learning scenarios over different devices and environments.

The European Commission is funding a large-scale research project on federated online laboratories (Labs) for education in Science, Technology, Engineering, and Mathematics (STEM) at School. The main educational focus is on inquiry learning and the main technological one is on personalized learning spaces. The learning spaces are offered through a single European social media portal supporting simultaneously teacher communities and student learning activities. This paper presents the general technical framework devised to support the construction and the exploitation of learning spaces, as well as the federation of online labs

This paper introduce the smart device paradigm and it usage to streamline the development of the next generation of online labs where more intelligence is placed at the server side and little or no assumption is made about the client. The requirements, the standards and protocols for implementation are presented. Finally a possible evolution toward Massive Open Online Labs (MOOLs) is discussed.

This paper proposes a novel approach to build and deploy learning analytics dashboards in multiple learning environments. Existing learning dashboards are barely portable: once deployed on a learning platform, it requires considerable effort to deploy the dashboard elsewhere. We suggest constructing dashboards from lightweight web applications, namely widgets. Our approach allows to port dashboards with no additional cost between learning environments that implement open specifications (Open Social and Activity Streams) for data access and use widget APIs. We propose to facilitate reuse by sharing the dashboards and widgets via a centralized analytics repository.

This paper reports on the successful use of Graasp, a social media platform, by university students for their collaborative work. Graasp features a number of innovations, such as administrator-free creation of collaborative spaces, a context-aware recommendation and privacy management. In the context of a EU-funded project involving large test beds, we have been able to extend this platform with lightweight tools (widgets) aimed for learning and competence development and to validate its usefulness in a collaborative learning context.

In this paper, we first discuss the concept of Personal Learning Environment (PLE) with respect to higher-education institutions and Virtual Learning Environments (VLEs). This discussion rapidly confronts us to the place of the PLE and self-directed learning and/or training inside the institution. We therefore introduce the concept of institutional PLE enabler, which is expected to stimulate students to create and use their own resources and institutional resources and share them with peers during formal and informal learning activities. Next, we describe a proposal for a federated design and implementation of the PLE enabler across multiple institutions.

In this paper we introduce a new decentralized navigation function for coordination of autonomous vehicles at intersections. The main contribution is a navigation function designed for vehicles with predefined paths that uses expected time to intersection for collision avoidance. In such way, deadlock situations are avoided. Different inertias of the vehicles are taken into account to enable on-board energy optimization for crossing. Heavier vehicles that need more energy and time for acceleration or braking are given an indirect priority at intersections. The proposed decentralized coordination scheme shows a significant improvement in energy consumption and in motion smoothness compared to traditional crossing with human drivers.

In this paper we compare the performance of autonomous vehicles at intersections with respect to the type of information shared. In addition to the position of other vehicles, in each simulated scenario one type of information is available. These types of information include the intended directions at intersection and the inertias of the other vehicles. An existing control method based on navigation functions is modified in order to benefit from the availability of such information. The results show that if autonomous vehicles know each other's inertia they achieve significantly smoother paths, use less fuel and avoid full stops.

This paper discusses the adoption of bottom- up social software tools in formal learning environments. This is believed to enhance the learning experience of today's young generation characterized by being technology savvy and keen on social networking. As a first step towards this objective, the 3A interaction model that aims at aiding the design of personal and collaborative learning platforms is presented. It accounts for interaction paradigms widely used in Web 2.0 applications and builds on Distributed Cognition and Activity Theory while remaining at the right level of abstraction to be easily ¿translatable¿ into tangible applications supporting both formal and informal learning.

This paper presents the findings of a study on the acceptability in higher education of a Web 2.0 collaborative application, namely eLogbook. The latter offers several features for sustaining collaboration and supporting personal and group learning. It was introduced to students taking a laboratory course that spans over one semester and mainly consists of in-class group experiments. In this paper, we present eLogbook. We then describe our hypotheses as well as the qualitative and quantitative methods used to evaluate the usefulness and usability of eLogbook in a formal learning context. Finally, we discuss our findings and its implications.

In this paper, we propose a personalized and contextual ranking algorithm implemented on top of the 3A interaction model. The latter is a generic model intended for designing and describing social and collaborative learning platforms integrating Actors, Assets and group Activities (the 3 "A"). The target user's interactions with his/her environment are modeled in a heterogeneous graph. Then, the algorithm is applied to simultaneously rank actors, assets and group activities taking into account the target user and his/her context. As an illustrative application and a preliminary evaluation, we apply the algorithm on data related to the activities carried out in a European Research Project, especially the collaboration between its members through the joint production of deliverables in workpackages.

Augmented Reality enhances the user perception by overlaying real world information with computer-generated information. In this paper, we study the user experience in Augmented Reality for the deployment of real-time adaptation. In the context of thin-client mobile Augmented Reality, the rate is often constrained due to limitations in the transmission link. End-to-end delay, frame rate, image size and head motion speed have been identified as important variables impacting the user experience. We propose a model to link the effect of these variables with the Quality of Experience metrics. Using this model, we present an adaptation scheme that adapts, in real time, the frame rate and the image size to maximize the Quality of Experience according to the context while satisfying the given rate constraint. Simulation shows the efficiency of the proposed scheme which achieves a better Quality of Experience than without adaptation. The adaptation still performs better than a solution with a fixed frame rate set to its maximum value.

Coordination of autonomous non-point agents in four-way crossings is studied in this work. A control scheme based on artificial potential functions is proposed in order to coordinate holonomic agents whose aim is to pass through an intersection while avoiding collisions. To do so, the agents are provided with a decentralized navigation function in which the goal and collision functions are decoupled. The local function defined on each agent requires no global knowledge on the desired destination of other agents. Furthermore, a reachability analysis via Lyapunov functions is proposed in order to guarantee marginal stability in our case study. In addition, conflict-free navigation and convergence properties are verified in simulation.

Coordination of autonomous non-point agents is studied in this work. Interactions between vehicles together with their internal dynamics have been modeled as a continuous, unconstrained differential game. In particular, collision prediction is shown to be achievable in a cooperative scheme through solving the boundary value equation associated with the dual problem. Optimal control formulation has been further applied in order to solve the coordination problem while coping with both time constraints and energy aspects of the system. The developed control scheme is further contrasted with the traditional negated gradient laws, which on the other hand can be shown to correspond to Nash Equilibria of non-cooperative strategies, in terms of its complexity and resultant trajectories. Furthermore, state feedback formation control is combined with the proposed cooperative scheme to alleviate its computational concerns. In addition, conflict-free navigation and convergence properties of the proposed scheme are verified in simulation.

In the framework of the European Integrated Project PALETTE, the Ecole Polytechnique Federale de Lausanne (EPFL) is developing the eLogbook Web 2.0 social software. The purpose of eLogbook is to support tacit and explicit knowledge management in communities of practice. It can be customized by the users to serve as an asset management system, as a task management system or as a discussion platform. In this paper, the innovative Computer-Human Interaction features of eLogbook are introduced and its deployment scenario to support collaborative laboratory activities in engineering education is described. The main idea is to sustain interaction for learning purpose within self-organized teams that integrate -on a seamless level- both human actors (students, teaching assistants) and non-human actors such as laboratory equipments or software agents.